All of Anders_H's Comments + Replies

Why did no LessWrong discourse on gain of function research develop in 2013/2014?

I found the original website for Prof. Lipsitch's "Cambridge Working Group" from 2014 at http://www.cambridgeworkinggroup.org/  . While the website does not focus exclusively on gain-of-function, this was certainly a recurring theme in his public talks about this. 

The list of signatories (which I believe has not been updated since 2016) includes several members of our community (apologies to anyone who I have missed):

  • Toby Ord, Oxford University
  • Sean O hEigeartaigh, University of Oxford
  • Daniel Dewey, University of Oxford
  • Anders Sandberg, Oxford Unive
... (read more)
2ChristianKl7moThat's interesting. That leaves the question of why the FHI mostly stopped caring about it after 2016. Past that point https://www.fhi.ox.ac.uk/wp-content/uploads/Lewis_et_al-2019-Risk_Analysis.pdf [https://www.fhi.ox.ac.uk/wp-content/uploads/Lewis_et_al-2019-Risk_Analysis.pdf] and https://www.fhi.ox.ac.uk/wp-content/uploads/C-Nelson-Engineered-Pathogens.pdf [https://www.fhi.ox.ac.uk/wp-content/uploads/C-Nelson-Engineered-Pathogens.pdf] seem to be about gain of function research while completely ignoring the issue of potential lab leaks and only talking about it as an interesting biohazard topic. My best guess is that it's like in math where applied researchers are lower status then theoretical researchers and thus everyone wants to be seen as addressing the theoretical issues. Infohazards are a great theoretical topic, discussing generalized methods to let researchers buy insurance for side effects of their research is a great theoretical topic as well. Given that Lipitch didn't talk directly about the gain of function research but tried to talk on a higher level to speak about more generalized solutions at EA Global Boston in 2017 he might have also felt social pressure to talk about the issue in a more theoretical manner then in a more applied manner where he told people about the risks of gain of function research. If we would have instead said on stage at EA Global Boston in 2017 "I believe that the risk of gain of function research is between 0.05% and 0.6% per fulltime researcher" this would have been awkward and create conflict that's uncomfortable. Talking about it in a more theoretical manner on the other hand allow a listener just to say "He Lipitch seems like a really smart guy". I don't want to say that as a critique of Lipitch, given that he actually did the best work. I however do think EA Global having a social structure that gets people to act that way is a systematic flaw. What do you think about that thesis?
Why did no LessWrong discourse on gain of function research develop in 2013/2014?

Here is a video of Prof. Lipsitch at EA Global Boston in 2017. I haven't watched it yet, but I would expect him to discuss gain-of-function research:  https://forum.effectivealtruism.org/posts/oKwg3Zs5DPDFXvSKC/marc-lipsitch-preventing-catastrophic-risks-by-mitigating

4ChristianKl7moHe only addresses it indirectly by saying we shouldn't develop very targeted approaches (which is what gain of function research is about) and instead fund interventions that are more broad. The talk doesn't mention the specific risk of gain of function research.
Why did no LessWrong discourse on gain of function research develop in 2013/2014?

Here is a data point not directly relevant to Less Wrong, but perhaps to the broader rationality community:  

Around this time, Marc Lipsitch organized a website and an open letter warning publicly about the dangers of gain-of-function research. I was a doctoral student at HSPH at the time, and shared this information with a few rationalist-aligned organizations. I remember making an offer to introduce them to Prof. Lipsitch, so that maybe he could give a talk. I got the impression that the Future of Life Institute had some communication with him, and ... (read more)

9ChristianKl7moWhile this crisis was a catastrophe and no existential challenge, it's unclear why that has to be generally the case. The claim that global catastrophic risk isn't part of the FLI mission seems strange to me. It's the thing the Global Priorities Project of CEA focus on (global catastrophic risk is more primarily mentioned on in the Global Priorities Project then X-risk). FLI does say on it's website that out of five areas [Future of Life Institute ] one of them is: It seems to me like an analysis that treats cloning (and climate change) as an X-risk but not gain of function research is seriously flawed. It does seem to me that the messed up in a major way and should do the 5 Why's just like OpenPhil should be required to do it. Having climate change as X-risk but not gain of function research suggests too much trust in experts and doing what's politically convienent instead of fighting the battles that are important. This was easy mode and they messed up. Donors to both donations should request analysis of what went wrong.
2Anders_H7moHere is a video of Prof. Lipsitch at EA Global Boston in 2017. I haven't watched it yet, but I would expect him to discuss gain-of-function research: https://forum.effectivealtruism.org/posts/oKwg3Zs5DPDFXvSKC/marc-lipsitch-preventing-catastrophic-risks-by-mitigating
Shall we count the living or the dead?

This comment touches on the central tension between the current paradigm in medicine, i.e. "evidence-based medicine" and an alternative and intuitively appealing approach based on a biological understanding of mechanism of disease.

In evidence-based medicine, decisions are based on statistical analysis of randomized trials;  what matters is whether we can be confident that the medication probabilistically has improved outcomes when tested on humans as a unit.  We don't care really care too much about the mechanism behind the causal effect, just wh... (read more)

Shall we count the living or the dead?

Thank you so much for writing this! Yes, this is mostly an accurate summary of my views (although I would certainly phrase some things differently). I just want to point out two minor disagreements:

  1. I don't think the problem is that doctors are too rushed to do a proper job, I think the patient-specific data that you would need is in many cases theoretically unobservable, or at least that we would need a much more complete understanding of biological mechanisms in order to know what to test the patients for in order to make a truly individualized decision.
... (read more)
Shall we count the living or the dead?

You are correct that someone who has one allergy may be more likely to have an other allergy, and that this violates the assumptions of our model. Our model relies on a strong independence assumption,  there are many realistic cases where this independence assumption will not hold.  I also agree that the video uses an example where the assumption may not hold. The video is oversimplified on purpose, in an attempt to get people interested enough to read the arXiv preprint.

If there is a small correlation between baseline risk and effect of treatmen... (read more)

Shall we count the living or the dead?

I very emphatically disagree with this. 

You are right that once you have a prediction for risk if untreated, and a prediction risk if treated, you just need a cost/benefit analysis. However, you won't get to that stage without a paradigm for extrapolation, whether implicit or explicit. I prefer making that paradigm explicit.

If you want to plug in raw experimental data, you are going to need data from people who are exactly like the patient in every way.  Then, you will be relying on a paradigm for extrapolation which claims that the conditional c... (read more)

Shall we count the living or the dead?

Suppose you summarize the effect of a drug using a relative risk (a multiplicative effect parameter relating the probability of the event if treated with the probability of the event if untreated), and consider this multiplicative parameter to represent the "magnitude of the effect"

The natural thing for a clinician to do will be to assume that the magnitude of the effect is the same in their own patients. They will therefore rely on this specific scale for extrapolation from the study to their patients. However, those patients may have a different risk pro... (read more)

8JenniferRM7moThanks! My next question was whether this was "just pedagogy and communication to help people avoid dumb calculation mistakes" or a real and substantive issue, and then I watched the video... And it is nice that the video is only 4:51 seconds long and works "just as well" on 2X... And... I think basically the claim is NOT just pedagogical, but substantive, but it was hard to notice. I've swirled your content around, and in doing so I feel like I've removed the stats confusion and turned it around so it sharpens the way the core question is about physical reality and modeling intuitions... Here is an alternative abstract that I offer for your use (as is, or with edits) if you want it <3 Does this seem like a friendly proposal (friendly to you, not friendly to the field, of course) for a better abstract, that focuses on a concrete example while pushing as hard as possible on the central substantive issue? I admit: peer reviewers would probably object to this. Also I did intentionally "punch it up" more than might be justified in hopes that you'll object in an informative way here and now. My abstract is PROBABLY WRONG (one example: I know your figure 1 is not a pearlian model) but I hope it is wrong in the way a bold sketch is less accurate than a more painterly depiction, while still being useful to figure out what the central message can or should be.
Shall we count the living or the dead?

No. This is not about interpretation of probabilities. It is about choosing what aspect of reality to rely on for extrapolation. You will get different extrapolations depending on whether you rely on a risk ratio, a risk difference or an odds ratio. This will lead to real differences in predictions for what happens under intervention.

Even if clinical decisions are entirely left to an algorithm, the algorithm will need to select a mathematical object to rely on for extrapolation. The person who writes the algorithm needs to tell the algorithm what to use, a... (read more)

1George7moIt is ultimately about interpretation. This paradigm doesn't matter if the physician has in mind a cost/benefit matrix for the treatment, in which it would be fairly easy to plug in raw experimental data no matter how the researchers chose to analyze it. More broadly, see the comment by ChristianKl.
Effect heterogeneity and external validity in medicine
Some time in the next week I'll write up a post with a few full examples (including the one from Robins, Hernan and Wasserman), and explain in a bit more detail.

I look forward to reading it. To be honest: Knowing these authors, I'd be surprised if you have found an error that breaks their argument.

We are now discussing questions that are so far outside of my expertise that I do not have the ability to independently evaluate the arguments, so I am unlikely to contribute further to this particular subthread (i.e. to the discussion about whether there exists an obvious and superior Bayesian solution to the problem I am trying to solve).

Effect heterogeneity and external validity in medicine

I don't have a great reference for this.

A place to start might be Judea Pearl's essay "Why I'm only half-Bayesian" at https://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf . If you look at his Twitter account at @yudapearl, you will also see numerous tweets where he refers to Bayes Theorem as a "trivial identity" and where he talks about Bayesian statistics as "spraying priors on everything". See for example https://twitter.com/yudapearl/status/1143118757126000640 and his discussions with Frank Harrell.

Ano... (read more)

Ok, after reading these, it's sounding a lot more like the main problem is causal inference people not being very steeped in Bayesian inference. Robins, Hernan and Wasserman's argument is based on a mistake that took all of ten minutes to spot: they show that a particular quantity is independent of propensity score function if the true parameters of the model are known, then jump to the estimate of that quantity being independent of propensity - when in fact, the estimate is dependent on the propensity, because the estimates of the model paramete... (read more)

Effect heterogeneity and external validity in medicine
Ok, I think that's the main issue here. As a criticism of Pearl and Bareinboim, I agree this is basically valid. That said, I'd still say that throwing out DAGs is a terrible way to handle the issue - Bayesian inference with DAGs is the right approach for this sort of problem.

I am not throwing out DAGs. I am just claiming that the particular aspect of reality that I think justifies extrapolation cannot be represented on a standard DAG. While I formalized my causal model for these aspects of reality without using graphs, I am confident that the... (read more)

4johnswentworth2yDo you know of any references on the problems people have run into? I've used Bayesian inference on causal models in my own day-to-day work quite a bit without running into any fundamental issues (other than computational difficulty), and what I've read of people using them in neuroscience and ML generally seems to match that. So it sounds like knowledge has failed to diffuse - either the folks using this stuff haven't heard about some class of problems with it, or the causal modelling folks are insufficiently steeped in Bayesian inference to handle the tricky bits.
Effect heterogeneity and external validity in medicine
Identifiability, sure. But latents still aren't a problem for either extrapolation or model testing, as long as we're using Bayesian inference. We don't need identifiability.

I am not using Bayesian inference, and neither are Pearl and Bareinboim. Their graphical framework ("selection diagrams") is very explicitly set up as model for reasoning about whether the causal effect in the target population is identified in terms of observed data from the study population and observed data from the target population. Such identification m... (read more)

6johnswentworth2yOk, I think that's the main issue here. As a criticism of Pearl and Bareinboim, I agree this is basically valid. That said, I'd still say that throwing out DAGs is a terrible way to handle the issue - Bayesian inference with DAGs is the right approach for this sort of problem. The whole argument about identification problems then becomes irrelevant, as it should. Sometimes the true model of reality is not identifiable. This is not a problem with reality, and pretending some other model generates reality is not the way to fix it. The way to fix it is to use an inference procedure which does not assume identifiability. The equality of this parameter is not sufficient to make the prediction we want to make - the counterfactual is still underspecified. The survival ratio calculation will only be correct if a particular DAG and counterfactual apply, and will be incorrect otherwise. By not using a DAG, it becomes unclear what assumptions we're even making - it's not at all clear what counterfactual we're using, or whether there even is a well-defined counterfactual for which the calculation is correct.
Effect heterogeneity and external validity in medicine
The key issue is that we're asking a counterfactual question. The question itself will be underdefined without the context of a causal model. The Russian roulette hypothetical is a good example: "Our goal is to find out what happens in Norway if everyone took up playing Russian roulette once a year". What does this actually mean? Are we asking what would happen if some mad dictator forced everyone to play Russian roulette? Or if some Russian roulette social media craze caught on? Or if people became suicidal en-masse and Russian roule
... (read more)
6johnswentworth2yAlright, updated on the chance that you actually know what you're doing, although I still think it's wildly unlikely that this Russian roulette example actually illustrates anything which is both useful and cannot be captured by DAGs. The obvious causal model for the Russian roulette example is one with four nodes: * first node indicating whether roulette is played * second node, child of first, indicating whether roulette killed * third node, child of second, indicating whether some other cause killed (can only happen if the person survived roulette) * fourth node, death, child of second and third node This makes sense physically, has a well-defined counterfactual for Norway, and produces the risk difference calculation from the post. What information is missing? Sure, the DAG isn't going to contain all the information - you'll usually have some information about the DAG, e.g. prior info about the DAG structure or about particular nodes within the DAG. But that's still info about the DAG - throwing away the DAG itself is still a step backwards. The underlying structure of reality is still a DAG, it's only our information about reality which will be non-DAG-shaped. DAGs show the causal structure, Bayesian probability handles whatever info we have about the DAGs. Identifiability, sure. But latents still aren't a problem for either extrapolation or model testing, as long as we're using Bayesian inference. We don't need identifiability.
Effect heterogeneity and external validity in medicine

I am curious why you think the approach based on causal diagrams is obviously correct. Would you be able to unpack this for me?

Does it not bother you that this approach fails to find a solution (i.e. won't make any predictions at all) if there are unmeasured causes of the outcome, even if treatment has no effect?

Does it not bother you that it fails to find a solution to the Russian roulette example, because the approach insists on treating "what happens if treated" and "what happens if untreated" as separate problems, and theref... (read more)

6johnswentworth2yThe key issue is that we're asking a counterfactual question. The question itself will be underdefined without the context of a causal model. The Russian roulette hypothetical is a good example: "Our goal is to find out what happens in Norway if everyone took up playing Russian roulette once a year". What does this actually mean? Are we asking what would happen if some mad dictator forced everyone to play Russian roulette? Or if some Russian roulette social media craze caught on? Or if people became suicidal en-masse and Russian roulette became popular accordingly? These are different counterfactuals, and the answer will be different depending on which of these we're talking about. We need the machinery of counterfactuals - and therefore the machinery of causal models - in order to define what we mean at all by "what happens in Norway if everyone took up playing Russian roulette once a year". That counterfactual only makes sense at all in the context of a causal model, and is underdefined otherwise. I assume by "unmeasured causes" you mean latent variables - i.e. variables in the causal graph which happened to not be observed. A causal diagram framework can handle latent variables just fine; there is no fundamental reason why every variable needs to be measured. Latent variables are a pain computationally, but they pose no fundamental problem mathematically. Indeed, much of machine learning consists of causal models with latent variables. Whether the treatment has an effect does not seem relevant here at all. No. My intuition very strongly says that 100% of the relevant structural information/model can be directly captured by causal models, and that you're just not used to encoding these sorts of intuitions into causal models. Indeed, counterfactuals are needed even to define what we mean, as in the Russian roulette example. The individual counterfactual distributions really are the thing we care about, and everything else is relevant only insofar as it approxima
The New Riddle of Induction: Neutral and Relative Perspectives on Color

In my view, "the problem of induction" is just a bunch of philosophers obsessing over the fact that induction is not deduction, and that you therefore cannot predict the future with logical certainty. This is true, but not very interesting. We should instead spend our energy thinking about how to make better predictions, and how we can evaluate how much confidence to have in our predictions. I agree with you that the fields you mention have made immense progress on that.

I am not convinced that computer programs are immune to Goodmans point. AI ... (read more)

The New Riddle of Induction: Neutral and Relative Perspectives on Color

I am not sure I fully understand this comment, or why you believe my argument is circular. It is possible that you are right, but I would very much appreciate a more thorough explanation.

In particular, I am not "concluding" that humans were produced by an evolutionary process; but rather using it as background knowledge. Moreover, this statement seems uncontroversial enough that I can bring it in as a premise without having to argue for it.

Since "humans were produced by an evolutionary process" is a premise and not a conclusion, I don't understand what you mean by circular reasoning.

Odds ratios and conditional risk ratios

Update: The editors of the Journal of Clinical Epidemiology have now rejected my second letter to the editor, and thus helped prove Eliezer's point about four layers of conversation.

Odds ratios and conditional risk ratios

Why do you think two senior biostats guys would disagree with you if it was obviously wrong? I have worked with enough academics to know that they are far far from infallible, but curious on your analysis of this question.

Good question. I think a lot of this is due to a cultural difference between those of us who have been trained in the modern counterfactual causal framework, and an old generation of methodologists who felt the old framework worked well enough for them and never bothered to learn about counterfactuals.

Odds ratios and conditional risk ratios

I wrote this on my personal blog; I was reluctant to post this to Less Wrong since it is not obviously relevant to the core interests of LW users. However, I concluded that some of you may find it interesting as an example of how the academic publishing system is broken. It is relevant to Eliezer's recent Facebook comments about building an intellectual edifice.

2NatashaRostova5yI thought it was interesting -- and frustrating for you. I haven't invested the time into proving to myself you're right, but in the case that you are I hope you're able to get someone to verify and lend you their credibility. Why do you think two senior biostats guys would disagree with you if it was obviously wrong? I have worked with enough academics to know that they are far far from infallible, but curious on your analysis of this question.

I wrote this on my personal blog; I was reluctant to post this to Less Wrong since it is not obviously relevant to the core interests of LW users. However, I concluded that some of you may find it interesting as an example of how the academic publishing system is broken. It is relevant to Eliezer's recent Facebook comments about building an intellectual edifice.

Is Caviar a Risk Factor For Being a Millionaire?

VortexLeague: Can you be a little more specific about what kind of help you need?

A very short, general introduction to Less Wrong is available at http://lesswrong.com/about/

Essentially, Less Wrong is a reddit-type forum for discussing how we can make our beliefs more accurate.

Choosing prediction over explanation in psychology: Lessons from machine learning

Thank you for the link, that is a very good presentation and it is good to see that ML people are thinking about these things.

There certainly are ML algorithms that are designed to make the second kind of predictions, but generally they only work if you have a correct causal model

It is possible that there are some ML algorithms that try to discover the causal model from the data. For example, /u/IlyaShpitser works on these kinds of methods. However, these methods only work to the extent that they are able to discover the correct causal model, so it seems disingenious to claim that we can ignore causality and focus on "prediction".

Choosing prediction over explanation in psychology: Lessons from machine learning

I skimmed this paper and plan to read it in more detail tomorrow. My first thought is that it is fundamentally confused. I believe the confusion comes from the fact that the word "prediction" is used with two separate meanings: Are you interested in predicting Y given an observed value of X (Pr[Y | X=x]), or are you interested in predicting Y given an intervention on X (i.e. Pr[Y|do(X=x)]).

The first of these may be useful for certain purposes. but If you intend to use the research for decision making and optimization (i.e. you want to interve... (read more)

1Kaj_Sotala5yNot entirely sure I understand you; I read the paper mostly as pointing out that current psych methodology tends to overfit, and that psychologists don't even know what overfitting means. This is true regardless of which type of prediction we're talking about.
0Vaniver5yI think there are ML algorithms that do figure out the second type. (I don't think this is simple conditioning, as jacob_cannell seems to be suggesting, but more like this [http://www.cs.nyu.edu/~shalit/slides.pdf].)
0jacob_cannell5yYes - general prediction - ie a full generative model - already can encompass causal modelling, avoiding any distinctions between dependent/independent variables: one can learn to predict any variable conditioned on all previous variables. For example, consider a full generative model of an ATARI game, which includes both the video and control input (from human play say). Learning to predict all future variables from all previous automatically entails learning the conditional effects of actions. For medicine, the full machine learning approach would entail using all available data (test measurements, diet info, drugs, interventions, whatever, etc) to learn a full generative model, which then can be conditionally sampled on any 'action variables' and integrated to generate recommended high utility interventions. In any practical near term system, sure. In theory though, a powerful enough predictor could learn enough of the world physics to invent de novo interventions wholecloth. ex: AlphaGo inventing new moves that weren't in its training set that it essentially invented/learned from internal simulations.
Triple or nothing paradox

Thanks for catching that, I stand corrected.

Triple or nothing paradox

The rational choice depends on your utility function. Your utility function is unlikely to be linear with money. For example, if your utility function is log (X), then you will accept the first bet, be indifferent to the second bet, and reject the third bet. Any risk-averse utility function (i.e. any monotonically increasing function with negative second derivative) reaches a point where the agent stops playing the game.

A VNM-rational agent with a linear utility function over money will indeed always take this bet. From this, we can infer that linear u... (read more)

4satt5yNot even that. You start with $1 (utility = 0) and can choose between 1. walking away with $1 (utility = 0), and 2. accepting a lottery with a 50% chance of leaving you with $0 (utility = −∞) and a 50% chance of having $3 (utility = log(3)). The first bet's expected utility is then −∞, and you walk away with the $1.
0Douglas_Knight5yYou are fighting the hypothetical. In the St Petersburg Paradox the casino is offering a fair bet, the kind that casinos offer. It is generally an error for humans to take these. In this scenario, the casino is magically tilting the bet in your favor. Yes, you should accept that bet and keep playing until the amount is an appreciable fraction of your net worth. But given that we are assuming the strange behavior of the casino, we could let the casino tilt the bet even farther each time, so that the bet has positive expected utility. Then the problem really is infinity, not utility. (Even agents with unbounded utility functions are unlikely to have them be unbounded as a function of money, but we could imagine a magical wish-granting genie.)
4AlexMennen5yNot true. It is true, however, that any agent with a bounded utility function eventually stops playing the game.
A quick note on weirdness points and Solstices [And also random other Solstice discussion]

Because I didn't perceive a significant disruption to the event, I was mentally bucketing you with people I know who severely dislike children and would secretly (or not so secretly) prefer that they not attend events like this at all; or that they should do so only if able to remain silent (which in practice means not at all.) I suspect Anders_H had the same reaction I did.

Just to be clear, I did not attend Solstice this year, and I was mentally reacting to a similar complaint that was made after last year's Solstice event. At last year's event, I did ... (read more)

0Bakkot5yWithout commenting on the merits and costs of children at Solstice or how they ought to be addressed: Having attended the East Bay solstice both this year and last, it was my impression that there was significantly more noise made by children during parts when the audience was otherwise quiet this year than there was last year. My recollection is hazy, but I'd guess it was maybe three to five times as much noise? In terms of number of distinct noisy moments and also volume. This year I was towards the back of the room; last year I was closer to the front.
A quick note on weirdness points and Solstices [And also random other Solstice discussion]

While I understand that some people may feel this way, I very much hope that this sentiment is rare. The presence of young children at the event only adds to the sense of belonging to a community, which is an important part of what we are trying to "borrow" from religions.

While I understand that some people may feel this way, I very much hope that this sentiment is rare.

There are two sentiments here, that I think should both be common:

  1. Children are welcome at community gatherings.

  2. There are times when children should be quiet or absent.

Raemon's solution is a good one here, as is a norm of parents removing children when they get especially loud. (At this event that may have been less practical than normal, given that it was about 40°F outside, but may have been solved by having a designated children's room or somethi... (read more)

When I still went to church, there was sometimes a "children's service" that went in parallel to the church service. Young kids who couldn't sit still and their parents were strongly encouraged to go to that one instead. It was a small room far enough away, with the pastor's wife, and I presume they were singing and nursing and changing diapers.

When a little child attended the Leipzig Solstice two years ago, that's basically what we did. She and her parents had their own room for themselves where they could go when she became tired and fussy. Tha... (read more)

I agree with you that having community events be family events is a very good idea and am also fully against the idea of banning children. Nevertheless, there is a big difference between some children making some noise during an event and a single child consistently talking and throwing fits interrupting half of the entire presentation while their parents don't remove from hearing range.

From what I've seen, it's not rare at all. I count... myself and at least 7 other people who've expressed the sentiment in private across both this year and last year (it happened last year too). It is, however, something that is very difficult for people to speak up about. I think what's going on is that different people care about differing portious of the solstice (community, message, aesthetics, etc) to surprisingly differing degrees, may have sensory sensitivites or difficulty with multiple audio input streams, and may or may not find children positiv... (read more)

I like having a community that supports children, but at the same time let's not close our eyes to the truth. If there actually is a child screaming throughout Solstice and running around rampant it will, in fact, ruin the experience. I don't know what the Bay Solstice was like, so I don't know if this was really the case or if it's an exaggeration.

Feature Wish List for LessWrong

I'd like each user to have their own sub domain (I.e such that my top level posts can be accessed either from Anders_h.lesswrong.com or from LW discussion). If possible it would be great if users could customize the design of their sub domain, such that posts look different when accessed from LW discussion.

This one equation may be the root of intelligence

Given that this was posted to LW, you'd think this link would be about a different equation..

0Pimgd5yNamely? Bayes? (TBH I wouldn't expect bayes because that'd be wrong, I think - you can have "dumb" intelligence based on reinforcement learning)
Open thread, Nov. 21 - Nov. 27 - 2016

The one-year embargo on my doctoral thesis has been lifted, it is now available at https://dash.harvard.edu/bitstream/handle/1/23205172/HUITFELDT-DISSERTATION-2015.pdf?sequence=1 . To the best of my knowledge, this is the first thesis to include a Litany of Tarski in the introduction.

On Trying Not To Be Wrong

Upvoted. I'm not sure how to phrase this without sounding sycophantic, but here is an attempt: Sarah's blog posts and comments were always top quality, but the last couple of posts seem like the beginning of something important, almost comparable to when Scott moved from squid314 to Slatestarcodex.

Open Thread, Sept 5. - Sept 11. 2016

Today, I uploaded a sequence of three working papers to my website at https://andershuitfeldt.net/working-papers/

This is an ambitious project that aims to change fundamental things about how epidemiologists and statisticians think about choice of effect measure, effect modification and external validity. A link to an earlier version of this manuscript was posted to Less Wrong half a year ago, the manuscript has since been split into three parts and improved significantly. This work was also presented in poster form at EA Global last month.

I want to give ... (read more)

Secret Rationality Base in Europe

This is almost certainly a small minority view, but from my perspective as a European based in the Bay Area who may be moving back to Europe next summer, the most important aspect would be geographical proximity to a decent university where staff and faculty can get away with speaking only English.

Why you should consider buying Bitcoin right now (Jan 2015) if you have high risk tolerance

What do you mean by "no risk"? This sentence seems to imply that your decisions are influenced by the sunk cost fallacy.

Try to imagine an alien who has been teleported into your body, who is trying to optimize your wealth. The fact that the coins were worth a third of their current price 18 months ago would not factor into the alien's decision.

Open thread, Jun. 13 - Jun. 19, 2016

There may be an ethically relevant distinction between a rule that tells you to avoid being the cause of bad things, and a rule that says you should cause good things to happen. However, I am not convinced that causality is relevant to this distinction. As far as I can tell, these two concepts are both about causality. We may be using words differently, do you think you could explain why you think this distinction is about causality?

2Daniel_Burfoot6yIn my understanding, consequentialism doesn't accept a moral distinction between sins of omission and sins of action. If a person dies whom I could have saved through some course of action, I'm just as guilty as I would be if I murdered the person. In my view, there must be a distinction between murder (=causing a death) and failure to prevent a death. If you want to be more formal, here's a good rule. Given a death, would the death still have a occurred in a counterfactual world where the potentially-guilty person did not exist? If the answer is yes, the person is innocent. Since lots of poor people would still be dying if I didn't exist, I'm thereby exonerated of their death (phew). I still feel bad about eating meat, though.
The Valentine’s Day Gift That Saves Lives

It would seem that the existence of such contractors follows logically from the fact that you are able to hire people despite the fact that you require contractors to volunteer 2/3 of their time.

2Gleb_Tsipursky6yThe issue at hand is motivation, not existence. Lumifer fails to understand that people would not work at 1/3 of the standard rate if they were not passionate about the cause.
Open Thread May 16 - May 22, 2016

The Economist published a fascinating blog entry where they use evidential decision theory to establish that tattoo removal results in savings to the prison system. See http://www.economist.com/blogs/freeexchange/2014/08/tattoos-jobs-and-recidivism . Temporally, this blog entry corresponds roughly to the time I lost my respect for the Economist. You can draw your own causal conclusions from this.

How do you learn Solomonoff Induction?

Solomonoff Induction is uncomputable, and implementing it will not be possible even in principle. It should be understood as an ideal which you should try to approximate, rather than something you can ever implement.

Solomonoff Induction is just bayesian epistemology with a prior determined by information theoretic complexity. As an imperfect agent trying to approximate it, you will get most of your value from simply grokking Bayesian epistemology. After you've done that, you may want to spend some time thinking about the philosophy of science of setting priors based on information theoretic complexity.

Open thread, Mar. 14 - Mar. 20, 2016

Thanks. Good points. Note that many of those words are already established in the literature with same meaning. For the particular example of "doomed", this is the standard term for this concept, and was introduced by Greenland and Robins (1986). I guess I could instead use "response type 1" but the word doomed will be much more effective at pointing to the correct concept, particularly for people who are familiar with the previous literature.

The only new term I introduce is "flip". I also provide a new definition of effect... (read more)

Open thread, Mar. 14 - Mar. 20, 2016

Do you mean probability instead of probably?

Yes. Thanks for noticing. I changed that sentence after I got the rejection letter (in order to correct a minor error that the reviewers correctly pointed out), and the error was introduced at that time. So that is not what they were referring to.

If the reviewers don't succeed in understanding what you are saying you might have explained yourself in casual language but still failed.

I agree, but I am puzzled by why they would have misunderstood. I spent a lot of effort over several months trying to be as... (read more)

0ChristianKl6yWhat kind of audience would you expect to understand your article?
Open thread, Mar. 14 - Mar. 20, 2016

Three days ago, I went through a traditional rite of passage for junior academics: I received my first rejection letter on a paper submitted for peer review. After I received the rejection letter, I forwarded the paper to two top professors in my field, who both confirmed that the basic arguments seem to be correct and important. Several top faculty members have told me they believe the paper will eventually be published in a top journal, so I am actually feeling more confident about the paper than before it got rejected.

I am also very frustrated with the ... (read more)

5Lumifer6yHaving glanced at your paper I think "too casual" means "your labels are too flippant" -- e.g. "Doomed". You're showing that you're human and that's a big no-no for a particular kind of people... By the way, you're entirely too fond of using quoted words ("flip", "transported", "monotonicity", "equal effects", etc.). If the word is not exactly right so that you have to quote it, find a better word (or make a footnote, or something). Frequent word quoting is often perceived as "I was too lazy to find the proper word, here is a hint, you guess what I meant".
1Douglas_Knight6yWithout reading your paper, and without rejecting your hypothesis, let me propose other consequences of casual language. Experts use tools casually, but there may be pitfalls for beginners. Experts are allowed more casual language and the referee may not trust that you, personally, are an expert. That is a signaling explanation, but somewhat different. A very different explanation is that while your ultimate goal is to teach the reader your casual process, but that does not mean that recording it is the best method. Your casual language may hide the pitfalls from beginners, contributing both to their incorrect usage and to their not understanding how to choose between tools. If your paper is aimed purely at experts, then casual language is the best means of communication. But should it be? Remember when you were a beginner. How did you learn the tools you are using? Did you learn them from papers aimed at beginners or experts; aimed at teaching tools or using them? Casual language papers can be useful for beginners as an advertisement: "Once you learn these tools, you can reason quickly and naturally, like me." Professors often say that they are surprised by which of their papers is most popular. In particular, they are often surprised that a paper that they thought was a routine application of a popular tool becomes popular as an exposition of that tool; often under the claim that it is a new tool. This is probably a sign that the system doesn't generate enough exposition, but taking the system as given, it means that an important purpose of research papers is exposition, that they really are aimed at beginners as well as experts. This is not to say that I endorse formal language. I don't think that formal language often helps the reader over the pitfalls; that work must be reconstructed by the reader regardless of whether it the author spelled it out. But I do think that it is important to point out the dangers..
1ChristianKl6yTo me that sentence seems cryptic. Do you mean probability instead of probably? Maybe the reviewer considered “flips” as too casual. I think the paper might be easier to read if you either would write flips directly without quotes or choose another word. What the difference between otherwise not have been a case and non-case? If the reviwers don't succeed in understanding what you are saying you might have explained yourself in casual language but still failed.
5ChristianKl6yIf my paper was rejected because it doesn't contain enough technical terms, I desire to believe that my paper was rejected because it doesn't contain enough technical terms; If my paper was not rejected because it doesn't contain enough technical terms, I desire to believe that my paper was not rejected because it doesn't contain enough technical terms; Let me not become attached to beliefs I may not want.
2Viliam6yDidn't read the paper, but I think a charitable explanation of "too casual" could mean (a) ambiguous, or (b) technically correct but not using the expressions standard in the field, so the reader needs a moment to understand "oh, what this paper calls X that's probably what most of us already call Y". But of course, I wouldn't dismiss the hypothesis of academically low-status language. Once at university I got a feedback about my essay that it's "technically correct, but this is not how university-educated people are supposed to talk". (Okay, I skimmed through your paper, and the language seemed fine. You sound like a human, as opposed to many other papers I have seen.)
Link: Evidence-Based Medicine Has Been Hijacked

I think the evidence for the effectiveness of statins is very convincing. The absolute risk reduction from statins will depend primarily on your individual baseline risk of coronary disease. From the information you have provided, I don't think your baseline risk is extraordinarily high, but it is also not negligible.

You will have to make a trade-off where the important considerations are (1) how bothered you are by the side-effects, (2) what absolute risk reduction you expect based on your individual baseline risk, (3) the marginal price (in terms of side... (read more)

0[anonymous]6yI don't want to complain about downvotes, but if someone believes that the above comment is misleading in any way I would like to hear the argument. I am mostly posting this because I have a strong hunch that if an admin looks up who downvoted the above comment , they will discover sockpuppet belonging to a man known by many names.
If there was one element of statistical literacy that you could magically implant in every head, what would it be?

Why do you want to be able to do that? Do you mean that you want to be able to look at a spreadsheet and move around numbers in your head until you know what the parameter estimates are? If you have access to a statistical software package, this would not give you the ability to do anything you couldn't have done otherwise. However, that is obvious, so I am going to assume you are more interested in groking some part of the underlying the epistemic process. But if that is indeed your goal, the ability to do the parameter estimation in your head seems like a very low priority, almost more of a party trick than actually useful.

0PhilGoetz6yI think it would be very useful. I have access to software packages, but it takes effort to gather data, type it in, etc. If I could do it in my head--my mind mentally keeping track of observations and updating the parameters as I go through life, for all sorts of questions--does it look like rain today? how energetic do I feel today? -- I'd be building accurate models of everything important in my life. It would be a different level of rationality.
Open Thread, Feb 8 - Feb 15, 2016

I disagree with this. In my opinion QALYs are much superior to DALYs for reasons that are inherent to how the measures are defined. I wrote a Tumblr post in response to Slatestarscratchpad a few weeks ago, see http://dooperator.tumblr.com/post/137005888794/can-you-give-me-a-or-two-good-article-on-why .

0[anonymous]6yI agree. QALY also relate to happiness, whereas DALY's relate to functioning. If you are a billionaire, you have a selfish incentive to reduce DALY's to raise productivity. QALY's are altruistic in the truest sense. I feel that should be pivotal in EA, not DALY's. Just because someone can function perfectly (no 'disability') doesn't mean there no super depressed and miserable. They, even if they are a millionaire banker, could be a lot worse off than a highly 'disabled' quadraplegic from the Ivory Coast.
The Fable of the Burning Branch

Richard, I don't think Less Wrong can survive losing both Ilya and you in the same week. I hope both of you reconsider. Either way, we definitely need to see this as a wake-up call. This forum has been in decline for a while, but this week I definitely think it hit a breaking point.

Interesting. The cynics are jumping ship.

But no. If Richard leaves because a heavily-downvoted article and the comments trying to direct its author to think a little bit deeper offends his sensibilities, that of course is his choice, but it says little about the forum as a whole. Like Ilya, I don't overly mind him, but he's also not a critical piece of the infrastructure; they both did little constructive work, and the forum is oversaturated with people willing to tell low-status members what they're doing wrong anyways, with nobody saying what they're doing right.

Lesswrong Survey - invitation for suggestions

How about asking "What is the single most important change that would make you want to participate more frequently on Less Wrong?"

This question would probably not be useful for the census itself, but it seems like a great opportunity to brainstorm..

Disguised Queries

I run the Less Wrong meetup group in Palo Alto. After we announced the events at Meetup.com, we often get a lot of guests who are interested in rationality but who have not read the LW sequences. I have an idea for a introductory session where we have the participants do a sorting exercise. Therefore, I am interested in getting 3D printed versions of rubes, bleggs and other items references in this post.

Does anyone have any thoughts on how to do this cheaply? Is there sufficient interest in this to get a kickstarter running? I expect that these items may be of interest to other Less Wrong meetup groups, and possibly to CFAR workshops and/or schools?

Clearing An Overgrown Garden

Less Wrong doesn't seem "overgrown" to me. It actually seems dried out and dying because the culture is so negative people don't want to post here. I believe Eliezer has talked about how whenever he posted something on LW, the comments would be full of people trying to find anything wrong with it.

"Overgrown" was probably a bad analogy, I tried too hard to reference the idea of well-kept gardens. What I was trying to say is that there are too many hostile elements who are making this website an unwelcoming place, by unnecessary criti... (read more)

Load More